Product Data Scientist - Causal Inference

We are bsport. The place to be!

bsport is an all-in-one platform combining boutique fitness and advanced technology. Our platform helps our partners manage their bookings, payroll, marketing and more, to streamline operations and boost their commercial success.

Since we launched in 2019, we have already achieved the following:

- We’ve built a community of over 7 million users 🤝

- In December 2024 we have finalised the biggest Series B (€30 million)🏆

- Grown our team to more than 200 employees 🚀

- Experienced strong worldwide growth (75% YoY) and rapid geographical expansion (+20 countries)

We’re continuing to grow to become the #1 tech partner for boutique studios in Europe and the rest of the world!

Join us to write the next chapter of your career!

The Data team turns bsport's data into better decisions for two audiences: bsport's own teams (leadership, product, CSM, sales, marketing, finance), and the studios themselves, through the reporting built into our product.

Over the past two years, we have built trusted foundations: a certified semantic layer with shared metric definitions, and an AI-powered self-serve that lets anyone find answers on their own.

We are now moving from describing what happened to proving what works and recommending what to do next. That is where you come in.

You will be the team's first Decision Scientist, with an analytics engineering mindset: you build the analysis-specific datasets you need on top of certified models, then turn them into decisions. You will own causal measurement at bsport: telling product and leadership which changes had real impact, for whom, and what to do next.

• Size opportunities before they are built: estimate the potential impact of roadmap bets so product can prioritize on evidence, not intuition.
• Make launches measurable by design, by partnering with product and engineering: progressive rollouts, tracking of who got what and when, success metrics agreed upfront.
• Own your analyses from plan to execution, including the data they rely on: eligibility, exposure and outcomes tables.
• Measure the real impact of product features on studio retention, revenue and activation, choosing the right method for each situation: experiments when we can randomise, observational methods when we cannot.
• Investigate when key metrics move unexpectedly: separate signal from noise, find root causes, and recommend what to do.
• Translate analytical findings into actionable recommendations, decision tools and guardrails.
• Raise statistical literacy across the company, and say clearly when the data cannot answer a question.
• First 3 months: you have agreed with product leads on the top decisions to measure, and have started an analysis on one of them, such as measuring how much a feature launch changed things for the studios that got it.
• First 6 months: new product launches always come with a measurement plan, and our Analytics Engineers reuse your methods.
• First year: our key initiatives of the year have a measured outcome, with a clear answer to "did it work, and for whom?", and these results feed our planning for the next year.

Stack

• Languages: SQL, Python
• Data platform: Databricks, Apache Airflow
• Transformation: dbt Core, medallion architecture (bronze, silver, gold)
• Data quality: dbt tests, Elementary
• BI & self-serve: Metabase, Sigma, Databricks Genie, internal AI data agents
• Experimentation & causal analysis: to be built with you
• Cloud: AWS

Our Hiring Process:

• 👋🏼 Interview with Talent Acquisition
• 🎙️ Manager interview (Live case study)
• 🧑🏽‍💻 Live Coding (SQL + Python)
• Interview with the Head of Data
• 📐 CPO interview (Stakeholder)
• 🤝 Reference checks

🌈🌍 Diversity is one of our most valuable assets, and we are committed to fostering an inclusive environment where everyone can contribute their best work. We welcome applicants from all backgrounds, identities, and experiences to help us build a more inclusive, equitable team.

If you’re excited about this role but don’t meet every qualification, we encourage you to apply-curiosity, adaptability, and a willingness to learn are just as important to us as specific skills.

• Have 4+ years in decision science, product data analytics or a similar role.
• Have hands-on experience with causal inference in a business setting (A/B tests, geo experiments, difference-in-differences, regression analysis…)
• Write production-grade SQL and have solid data modeling experience.
• Are fluent in Python for statistical analysis.
• Have delivered analyses that changed real decisions, and pick the simplest method that is good enough.
• Can explain uncertainty in plain language, and turn a confidence interval into a recommendation a PM or CPO can act on.
• Enjoy autonomy and ownership.
• Experience in B2B SaaS or marketplace products.
• Worked with Databricks and dbt.

• Start from solid ground: our data models are reliable and tested, so you spend your time on analysis, not on cleaning data.
• See your impact: your work feeds straight into product roadmap decisions.
• Build the function: this is a new role at bsport, with room to shape the methods and tools.

We believe great work comes from happy, supported people-that’s why we offer meaningful perks designed to promote balance, growth, and connection.

Competitive salary packages based on your experience and role.

💻 Work-Life harmony

Hybrid model with 3 days per week in the office

🌎 Work from anywhere

Enjoy up to 15 days of remote work from abroad each year.

Stay active with a specially discounted HelloCSE membership.

🌍 Diverse fun loving team

Multicultural colleagues, after-work events, team-building & more.